Author

Zefeng Bai

Date of Award

2022

Document Type

Dissertation

Degree Name

PhD in Business

Department

Department of Mathematical Sciences: Business Analytics

First Advisor

Victoria Steblovskaya

Second Advisor

Yicheng Kang

Third Advisor

Jahangir Sultan

Fourth Advisor

Dessislava Pachamanova

Abstract

The target volatility strategy is a very popular investment concept in financial marketplace. For my dissertation, I focus on studying the target volatility investment concept in application to pension accumulation as well as decumulation stages. Additionally, I extend a basic target volatility strategy by introducing trading boundaries to its asset allocation mechanism. My dissertation study follows a three-paper format.

In paper one, we propose a new pension strategy that aims at improving the protection of a long-term pension plan in volatile market conditions. Over a hypothetical twenty-year pension scheme, we show that our newly proposed strategy, which attaches a target volatility mechanism to a lifecycle strategy, could provide more effective capital protection and risk control for pension investment vehicles. In addition, we show that our proposed strategy has an improved portfolio diversification effect and market timing skills compared to a benchmark pension strategy. Our results are robust with a consideration of transaction costs.

In paper two, we enhance the retirement coverage of several conventional retirement plans by using a target volatility strategy with interest rate dependent target volatility levels. Using the Monte Carlo simulation approach, we find that the retirement portfolio enhanced by the target volatility mechanism shows a significantly higher level of confidence to achieve required income levels compared to the conventional retirement portfolio. Therefore, the target volatility investment strategy could be a suitable alternative for investors who look for a higher level of stability in retirement coverage.

In paper three, we attempt to reduce the transaction costs of a target volatility portfolio by adding market risk calibrated rebalancing boundaries to its asset allocation mechanism. A constraint optimization problem based on investor-relevant optimization criteria is formulated. A numerical optimization algorithm to find an optimal rebalancing boundary level is presented. Illustrative numerical results within the Black-Scholes environment, as well as using real market data are reported. The comparative analysis on different market scenarios suggests that the target volatility portfolio with rebalancing boundaries can effectively reduce portfolio transaction costs and improve portfolio returns. Our findings have important applications given the popularity of the target volatility investment strategies among financial practitioners.

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